InfleCS: Clustering Free Energy Landscapes with Gaussian Mixtures

Development, validation and analysis of a human profurin 3D model using comparative modeling and molecular dynamics simulations

The emergence of new viruses can lead to the outbreak of pandemics as occurred at the end of 2019 with the coronavirus disease (or COVID-19). The fastest way to effectively control viral infections is to develop broad-spectrum antivirals that can fight at least an entire class of viruses. Profurin, the furin precursor propeptide, is responsible for the autoactivation step which is crucial for the maturation of several viral substrates. This role makes the study of furin and profurin interactions interesting for the development of new potential broad-spectrum antivirals for the treatment against several human viral diseases. Since there is no 3D model of profurin published in the literature or deposited in a database, this work reports the development, validation and analysis of a profurin 3D model using comparative modeling and molecular dynamics. The model is available in ModelArchive at https://www.modelarchive.org/doi/10.5452/ma-ct8l7. The usage of this model will make possible further studies of molecular docking and MD simulations of the profurin-furin system, in the design of new potential broad-spectrum antivirals for the treatment against several human viral diseases.Communicated by Ramaswamy H. Sarma.

Structural Dynamics Analysis of USP14 Activation by AKT-Mediated Phosphorylation

Ubiquitin-specific protease 14 (USP14), one of the three major proteasome-associated deubiquitinating enzymes (DUBs), is known to be activated by the AKT-mediated phosphorylation at Ser432. Thereby, AKT can regulate global protein degradation by controlling the ubiquitin-proteasome system (UPS). However, the exact molecular mechanism of USP14 activation by AKT phosphorylation at the atomic level remains unknown. By performing the molecular dynamics (MD) simulation of the USP14 catalytic domain at three different states (inactive, active, and USP14-ubiquitin complex), we characterized the change in structural dynamics by phosphorylation. We observed that the Ser432 phosphorylation induced substantial conformational changes of USP14 in the blocking loop (BL) region to fold it from an open loop into a β-sheet, which is critical for USP14 activation. Furthermore, phosphorylation also increased the frequency of critical hydrogen bonding and salt bridge interactions between USP14 and ubiquitin, which is essential for DUB activity. Structural dynamics insights from this study pinpoint the important local conformational landscape of USP14 by the phosphorylation event, which would be critical for understanding USP14-mediated proteasome regulation and designing future therapeutics.

Coevolution-Driven Method for Efficiently Simulating Conformational Changes in Proteins Reveals Molecular Details of Ligand Effects in the β2AR Receptor

With the advent of AI-powered structure prediction, the scientific community is inching closer to solving protein folding. An unresolved enigma, however, is to accurately, reliably, and deterministically predict alternative conformational states that are crucial for the function of, e.g., transporters, receptors, or ion channels where conformational cycling is innately coupled to protein function. Accurately discovering and exploring all conformational states of membrane proteins has been challenging due to the need to retain atomistic detail while enhancing the sampling along interesting degrees of freedom. The challenges include but are not limited to finding which degrees of freedom are relevant, how to accelerate the sampling along them, and then quantifying the populations of each micro- and macrostate. In this work, we present a methodology that finds relevant degrees of freedom by combining evolution and physics through machine learning and apply it to the conformational sampling of the β2 adrenergic receptor. In addition to predicting new conformations that are beyond the training set, we have computed free energy surfaces associated with the protein's conformational landscape. We then show that the methodology is able to quantitatively predict the effect of an array of ligands on the β2 adrenergic receptor activation through the discovery of new metastable states not present in the training set. Lastly, we also stake out the structural determinants of activation and inactivation pathway signaling through different ligands and compare them to functional experiments to validate our methodology and potentially gain further insights into the activation mechanism of the β2 adrenergic receptor.

Computational and structural insights into the pre- and post-hydrolysis states of bovine multidrug resistance-associated protein 1

ATP-binding cassette C-family drug membrane transporters play an important role in local pharmacokinetics, that is, drug concentration in cellular compartments. From the structural point of view, only the bovine ortholog of the multidrug resistance-associated protein 1 (bMRP1) has been resolved. We here used μs-scaled molecular dynamics simulations to investigate the structure and dynamics of the bovine multidrug resistance-associated protein 1 in pre- and post-hydrolysis functional states. The present work aims to examine the slight but likely relevant structural differences between pre- and post-hydrolysis states of outward-facing conformations as well as the interactions between the multidrug resistance-associated protein 1 and the surrounding lipid bilayer. Global conformational dynamics show unfavourable extracellular opening associated with nucleotide-binding domain dimerization indicating that the post-hydrolysis state adopts a close-cleft conformation rather than an outward-open conformation. Our present simulations also highlight persistent interactions with annular cholesterol molecules and the expected active role of lipid bilayer in the allosteric communication between distant domains of the transporter.

Ni2+ and Cu2+ complexes of N-(2,6-dichlorophenyl)-N-mesityl formamidine dithiocarbamate structural and functional properties as CYP3A4 potential substrates

Metal compounds continued to attract diverse applications due to their malleability in several capacities. In this study, we present our findings on the crystal structures and functional properties of Ni2+ and Cu2+ complexes of N'-(2,6-dichlorophenyl)-N-mesitylformamidine dithiocarbamate (L) comprising [Ni-(L)2] (1) and [Cu-(L)2] (2) with a four-coordinate metal center. We established the two complex structures through 1H and 13C nuclear magnetic resonance (NMR), elemental, and single-crystal X-ray analysis. The analyses showed that the two complexes are isomorphous, having P21/c as a space group and a unit-cell similarity index (π) of 0.002. The two complexes conform to a distorted square planar geometry around the metal centers. The calculated and experimental data, including bond lengths, angles, and NMR values, are similar. Hirshfeld surface analysis revealed the variational contribution of the different types of intermolecular contacts driven by the crystal lattice of the two solvated complexes. Our knowledge of the potential biological implication of these structures enabled us to probe the compounds as prospective CYP3A4 inhibitors. This approach mimics current trends in pharmaceutical design and biomedicine by incorporating potentially active molecules into various media to predict their biological efficacies. The simulations show appreciable binding of compounds 1 and 2 to CYP3A4 with average interaction energies of -97 and -87 kcal/mol, respectively. The protein attains at least five conformational states in the three studied models using a Gaussian Mixture Model-based clustering and free energy prediction. Electric field analysis shows the crucial residues to substrate binding at the active site, enabling CYP3A4 structure to function prediction. The predicted inhibition with these Ni2+ and Cu2+ complexes indicates that CYP3A4 overexpression in a diseased state like cancer would reduce, thereby increasing the chemotherapeutic compounds' shelf-lives for adsorption. This multidimensional study addresses various aspects of molecular metal electronics, including their application as substrate-mimicking inhibitors. The outcome would enable further research on bio-metal compounds of critical potential.

Toward a Benchmark for Markov State Models: The Folding of HP35

Adopting a 300 μs long MD trajectory of the folding of villin headpiece (HP35) by D. E. Shaw Research, we recently constructed a Markov state model (MSM) based on inter-residue contacts. The model reproduces the folding time and predicts that the native basin and unfolded region consist of metastable substates that are structurally well-characterized. Recognizing the need to establish well-defined benchmark problems, we study to what extent and in what sense this MSM can be employed as a reference model. Hence, we test the robustness of the MSM by comparing it to models that use alternative combinations of features, dimensionality reduction methods, and clustering schemes. The study suggests some main characteristics of the folding of HP35 that should be reproduced by other competitive models. Moreover, the discussion reveals which parts of the MSM workflow matter most for the considered problem and illustrates the promises and pitfalls of state-based models for the interpretation of biomolecular simulations.

Reaction Coordinates for Conformational Transitions Using Linear Discriminant Analysis on Positions

In this work, we demonstrate that Linear Discriminant Analysis (LDA) applied to atomic positions in two different states of a biomolecule produces a good reaction coordinate between those two states. Atomic coordinates of a macromolecule are a direct representation of a macromolecular configuration, and yet, they are not used in enhanced sampling studies due to a lack of rotational and translational invariance. We resolve this issue using the technique of our prior work, whereby a molecular configuration is considered a member of an equivalence class in size-and-shape space, which is the set of all configurations that can be translated and rotated to a single point within a reference multivariate Gaussian distribution characterizing a single molecular state. The reaction coordinates produced by LDA applied to positions are shown to be good reaction coordinates both in terms of characterizing the transition between two states of a system within a long molecular dynamics (MD) simulation and also ones that allow us to readily produce free energy estimates along that reaction coordinate using enhanced sampling MD techniques.

Oncogenic CALR mutant C-terminus mediates dual binding to the thrombopoietin receptor triggering complex dimerization and activation

Calreticulin (CALR) frameshift mutations represent the second cause of myeloproliferative neoplasms (MPN). In healthy cells, CALR transiently and non-specifically interacts with immature N-glycosylated proteins through its N-terminal domain. Conversely, CALR frameshift mutants turn into rogue cytokines by stably and specifically interacting with the Thrombopoietin Receptor (TpoR), inducing its constitutive activation. Here, we identify the basis of the acquired specificity of CALR mutants for TpoR and define the mechanisms by which complex formation triggers TpoR dimerization and activation. Our work reveals that CALR mutant C-terminus unmasks CALR N-terminal domain, rendering it more accessible to bind immature N-glycans on TpoR. We further find that the basic mutant C-terminus is partially α-helical and define how its α-helical segment concomitantly binds acidic patches of TpoR extracellular domain and induces dimerization of both CALR mutant and TpoR. Finally, we propose a model of the tetrameric TpoR-CALR mutant complex and identify potentially targetable sites.

Reconstructing the infrared spectrum of a peptide from representative conformers of the full canonical ensemble

Leucine enkephalin (LeuEnk), a biologically active endogenous opioid pentapeptide, has been under intense investigation because it is small enough to allow efficient use of sophisticated computational methods and large enough to provide insights into low-lying minima of its conformational space. Here, we reproduce and interpret experimental infrared (IR) spectra of this model peptide in gas phase using a combination of replica-exchange molecular dynamics simulations, machine learning, and ab initio calculations. In particular, we evaluate the possibility of averaging representative structural contributions to obtain an accurate computed spectrum that accounts for the corresponding canonical ensemble of the real experimental situation. Representative conformers are identified by partitioning the conformational phase space into subensembles of similar conformers. The IR contribution of each representative conformer is calculated from ab initio and weighted according to the population of each cluster. Convergence of the averaged IR signal is rationalized by merging contributions in a hierarchical clustering and the comparison to IR multiple photon dissociation experiments. The improvements achieved by decomposing clusters containing similar conformations into even smaller subensembles is strong evidence that a thorough assessment of the conformational landscape and the associated hydrogen bonding is a prerequisite for deciphering important fingerprints in experimental spectroscopic data.

On the interplay between lipids and asymmetric dynamics of an NBS degenerate ABC transporter

Multidrug resistance-associated proteins are ABC C-family exporters. They are crucial in pharmacology as they transport various substrates across membranes. However, the role of the degenerate nucleotide-binding site (NBS) remains unclear likewise the interplay with the surrounding lipid environment. Here, we propose a dynamic and structural overview of MRP1 from ca. 110 μs molecular dynamics simulations. ATP binding to NBS1 is likely maintained along several transport cycles. Asymmetric NBD behaviour is ensured by lower signal transduction from NBD1 to the rest of the protein owing to the absence of ball-and-socket conformation between NBD1 and coupling helices. Even though surrounding lipids play an active role in the allosteric communication between the substrate-binding pocket and NBDs, our results suggest that lipid composition has a limited impact, mostly by affecting transport kinetics. We believe that our work can be extended to other degenerate NBS ABC proteins and provide hints for deciphering mechanistic differences among ABC transporters.

Prediction of Aptamer-Small-Molecule Interactions Using Metastable States from Multiple Independent Molecular Dynamics Simulations

Understanding aptamer-ligand interactions is necessary to rationally design aptamer-based systems. Commonly used in silico tools have proven to be accurate to predict RNA and DNA oligonucleotide tertiary structures. However, given the complexity of nucleic acids, the most thermodynamically stable conformation is not necessarily the one with the highest affinity for a specific ligand. Because many metastable states may coexist, it remains challenging to predict binding sites through molecular docking simulations using available computational pipelines. In this study, we used independent simulations to broaden the conformational diversity sampled from DNA initial models of distinct stability and assessed the binding affinity of selected metastable representative structures. In our results, utilizing multiple metastable conformations for molecular docking analysis helped identify structures favorable for ligand binding and accurately predict the binding sites. Our workflow was able to correctly identify the binding sites of the characterized adenosine monophosphate and l-argininamide aptamers. Additionally, we demonstrated that our pipeline can be used to aid the design of competition assays that are conducive to aptasensing strategies using an uncharacterized aflatoxin B1 aptamer. We foresee that this approach may help rationally design effective and truncated aptamer sequences interacting with protein biomarkers or small molecules of interest for drug design and sensor applications.

Quantitative Agreement between Conformational Substates of Holo Calcium-Loaded Calmodulin Detected by Double Electron-Electron Resonance EPR and Predicted by Molecular Dynamics Simulations

Calcium-loaded calmodulin (CaM/4Ca²⁺) comprises two domains that undergo rigid body reorientation from a predominantly extended conformation to a compact one upon binding target peptides. A recent replica-exchange molecular dynamics (MD) simulation on holo CaM/4Ca²⁺ suggested the existence of distinct structural clusters (substates) along the path from extended to compact conformers in the absence of substrates. Here, we experimentally demonstrate the existence of CaM/4Ca²⁺ substates trapped in local minima by three freezing/annealing regimes (slow, 40 s; intermediate, 1.5 s; fast, 0.5 ms) using pulsed Q-band double electron-electron resonance (DEER) EPR spectroscopy to measure interdomain distances between nitroxide spin-labels positioned at A17C and A128C in the N- and C-terminal domains, respectively. The DEER echo curves were directly fit to population-optimized P(r) pairwise distance distributions calculated from the coordinates of the MD clusters and compact crystal structure. DEER data on fully deuterated CaM/4Ca²⁺ were acquired at multiple values of the second echo period (10-35 μs) and analyzed globally to eliminate instrumental and overfitting artifacts and ensure accurate populations, peak positions, and widths. The DEER data for all three freezing regimes are quantitatively accounted for within experimental error by 5-6 distinct conformers comprising a predominantly populated extended form (60-75%) and progressively more compact states whose populations decrease as the degree of compactness increases. The shortest interdomain separation is found in the compact crystal structure, which has an occupancy of 4-6%. Thus, CaM/4Ca²⁺ samples high energy local minima comprising a few discrete substates of increasing compactness in a rugged energy landscape.

Size-and-Shape Space Gaussian Mixture Models for Structural Clustering of Molecular Dynamics Trajectories

Determining the optimal number and identity of structural clusters from an ensemble of molecular configurations continues to be a challenge. Recent structural clustering methods have focused on the use of internal coordinates due to the innate rotational and translational invariance of these features. The vast number of possible internal coordinates necessitates a feature space supervision step to make clustering tractable but yields a protocol that can be system type-specific. Particle positions offer an appealing alternative to internal coordinates but suffer from a lack of rotational and translational invariance, as well as a perceived insensitivity to regions of structural dissimilarity. Here, we present a method, denoted shape-GMM, that overcomes the shortcomings of particle positions using a weighted maximum likelihood alignment procedure. This alignment strategy is then built into an expectation maximization Gaussian mixture model (GMM) procedure to capture metastable states in the free-energy landscape. The resulting algorithm distinguishes between a variety of different structures, including those indistinguishable by root-mean-square displacement and pairwise distances, as demonstrated on several model systems. Shape-GMM results on an extensive simulation of the fast-folding HP35 Nle/Nle mutant protein support a four-state folding/unfolding mechanism, which is consistent with previous experimental results and provides kinetic details comparable to previous state-of-the art clustering approaches, as measured by the VAMP-2 score. Currently, training of shape-GMMs is recommended for systems (or subsystems) that can be represented by ≲200 particles and ≲100k configurations to estimate high-dimensional covariance matrices and balance computational expense. Once a shape-GMM is trained, it can be used to predict the cluster identities of millions of configurations.

Insights into the structure and function of the human organic anion transporter 1 in lipid bilayer membranes

The human SLC22A6/OAT1 plays an important role in the elimination of a broad range of endogenous substances and xenobiotics thus attracting attention from the pharmacological community. Furthermore, OAT1 is also involved in key physiological events such as the remote inter-organ communication. Despite its significance, the knowledge about hOAT1 structure and the transport mechanism at the atomic level remains fragmented owing to the lack of resolved structures. By means of protein-threading modeling refined by μs-scaled Molecular Dynamics simulations, the present study provides the first robust model of hOAT1 in outward-facing conformation. Taking advantage of the AlphaFold 2 predicted structure of hOAT1 in inward-facing conformation, we here provide the essential structural and functional features comparing both states. The intracellular motifs conserved among Major Facilitator Superfamily members create a so-called “charge-relay system” that works as molecular switches modulating the conformation. The principal element of the event points at interactions of charged residues that appear crucial for the transporter dynamics and function. Moreover, hOAT1 model was embedded in different lipid bilayer membranes highlighting the crucial structural dependence on lipid-protein interactions. MD simulations supported the pivotal role of phosphatidylethanolamine components to the protein conformation stability. The present model is made available to decipher the impact of any observed polymorphism and mutation on drug transport as well as to understand substrate binding modes.

Human Vitamin K Epoxide Reductase as a Target of Its Redox Protein

Human vitamin K epoxide reductase (hVKORC1) enzymatic activity requires an initial activation by a specific redox protein, a less studied step in the hVKORC1 vital cycle. Significant steric conditions must be met by enzymes, being that to adapt their configurations is mandatory for hVKORC1 activation. We studied, by molecular dynamics (MD) simulations, the folding and conformational plasticity of hVKORC1 in its inactive (fully oxidised) state using available structures, crystallographic and from de novo modelling. According to the obtained results, hVKORC1 is a modular protein composed of the stable transmembrane domain (TMD) and intrinsically disordered luminal (L) loop, possessing the great plasticity/adaptability required to perform various steps of the activation process. The docking (HADDOCK) of Protein Disulfide Isomerase (PDI) onto different hVKORC1 conformations clearly indicated that the most interpretable solutions were found on the target closed L-loop form, a prevalent conformation of hVKORC1’s oxidised state. We also suggest that the cleaved L-loop is an appropriate entity to study hVKORC1 recognition/activation by its redox protein. Additionally, the application of hVKORC1 (membrane protein) in aqueous solution is likely to prove to be very useful in practice in either in silico studies or in vitro experiments.

Elucidating the Structural Features of ABCA1 in its Heterogeneous Membrane Environment

ATP Binding Cassette Transporter A1 (ABCA1) plays an integral part in Reverse Cholesterol Transport (RCT) and is critical for maintaining lipid homeostasis. One theory of lipid efflux by the transporter (alternating access) proposes that ABCA1 harbours two different conformations that provide alternating access for lipid binding and release. This is followed by sequestration via a direct interaction between ABCA1 and its partner, ApoA1. The other theory (lateral access) proposes that ABCA1 obtains lipids laterally from the membrane to form a temporary extracellular “reservoir”. This reservoir contains an isolated lipid monolayer due to the net accumulation of lipids in the exofacial leaflet. Recently, a full-length Cryo-EM structure of this 2,261-residue transmembrane protein showed its discreetly folded domains and have detected the presence of a tunnel enclosed within the extracellular domains (ECDs) but not in the TMDs, giving it an outward-facing conformation. This structure was hypothesized to substantiate the lateral access theory. Utilizing long time-scale multiple replica atomistic molecular dynamics simulations (MDS), we simulated the structure in a large heterogeneous lipid environment and found that the protein undergoes several large conformational changes in its extremities. We observed that the cavity enclosed within ATP unbound form of ABCA1 is narrow at the distal ends of TMD as well as the ECD region substantiating the “lateral access” theory. We have also characterized ABCA1 and the lipid dynamics along with the protein-lipid interactions in the heterogeneous environment, providing novel insights into understanding ABCA1 conformation at an atomistic level.

Variational embedding of protein folding simulations using Gaussian mixture variational autoencoders

Conformational sampling of biomolecules using molecular dynamics simulations often produces a large amount of high dimensional data that makes it difficult to interpret using conventional analysis techniques. Dimensionality reduction methods are thus required to extract useful and relevant information. Here, we devise a machine learning method, Gaussian mixture variational autoencoder (GMVAE), that can simultaneously perform dimensionality reduction and clustering of biomolecular conformations in an unsupervised way. We show that GMVAE can learn a reduced representation of the free energy landscape of protein folding with highly separated clusters that correspond to the metastable states during folding. Since GMVAE uses a mixture of Gaussians as its prior, it can directly acknowledge the multi-basin nature of the protein folding free energy landscape. To make the model end-to-end differentiable, we use a Gumbel-softmax distribution. We test the model on three long-timescale protein folding trajectories and show that GMVAE embedding resembles the folding funnel with folded states down the funnel and unfolded states outside the funnel path. Additionally, we show that the latent space of GMVAE can be used for kinetic analysis and Markov state models built on this embedding produce folding and unfolding timescales that are in close agreement with other rigorous dynamical embeddings such as time independent component analysis.

FSATOOL 2.0: An integrated molecular dynamics simulation and trajectory data analysis program

Molecular dynamics simulation is important in the computational study of the biomolecules. In this paper, we upgrade our previous FSATOOL to version 2.0. It is no longer a plugin as before. Besides the existed enhanced sampling and Markov state model analysis module, FSATOOL 2.0 has three new features now. First, it contains a molecular dynamics simulation engine on both CPU and GPU device. The engine works with an embedded enhanced sampling module. Second, it can do the free energy calculation by various practical methods, including the weighted histogram analysis method and Gaussian mixture model. Third, it has many subroutines to process the trajectory data, such as principal component analysis, time-structure based independent component analysis, contact analysis, and Φ-value analysis. Most importantly, all these calculations are integrated into one package. The trajectory data format is compatible with all the modules. With a proper input parameter file, users can do the molecular dynamics simulation and data analysis work by only a few simplified commands. The capabilities and theoretical backgrounds of FSATOOL 2.0 are introduced in the paper.

Molecular dynamics modeling of the Vibrio cholera Na+-translocating NADH:quinone oxidoreductase NqrB-NqrD subunit interface

The Na⁺-translocating NADH:quinone oxidoreductase (Na⁺-NQR) is the major Na⁺ pump in aerobic pathogens such as Vibrio cholerae. The interface between two of the NQR subunits, NqrB and NqrD, has been proposed to harbor a binding site for inhibitors of Na⁺-NQR. While the mechanisms underlying Na⁺-NQR function and inhibition remain underinvestigated, their clarification would facilitate the design of compounds suitable for clinical use against pathogens containing Na⁺-NQR. An in silico model of the NqrB-D interface suitable for use in molecular dynamics simulations was successfully constructed. A combination of algorithmic and manual methods was used to reconstruct portions of the two subunits unresolved in the published crystal structure and validate the resulting structure. Hardware and software optimizations that improved the efficiency of the simulation were considered and tested. The geometry of the reconstructed complex compared favorably to the published V. cholerae Na⁺-NQR crystal structure. Results from one 1 µs, three 150 ns and two 50 ns molecular dynamics simulations illustrated the stability of the system and defined the limitations of this model. When placed in a lipid bilayer under periodic boundary conditions, the reconstructed complex was completely stable for at least 1 µs. However, the NqrB-D interface underwent a non-physiological transition after 350 ns.

Unsupervised Learning Methods for Molecular Simulation Data

Unsupervised learning is becoming an essential tool to analyze the increasingly large amounts of data produced by atomistic and molecular simulations, in material science, solid state physics, biophysics, and biochemistry. In this Review, we provide a comprehensive overview of the methods of unsupervised learning that have been most commonly used to investigate simulation data and indicate likely directions for further developments in the field. In particular, we discuss feature representation of molecular systems and present state-of-the-art algorithms of dimensionality reduction, density estimation, and clustering, and kinetic models. We divide our discussion into self-contained sections, each discussing a specific method. In each section, we briefly touch upon the mathematical and algorithmic foundations of the method, highlight its strengths and limitations, and describe the specific ways in which it has been used-or can be used-to analyze molecular simulation data.

"Dividing and Conquering" and "Caching" in Molecular Modeling

Molecular modeling is widely utilized in subjects including but not limited to physics, chemistry, biology, materials science and engineering. Impressive progress has been made in development of theories, algorithms and software packages. To divide and conquer, and to cache intermediate results have been long standing principles in development of algorithms. Not surprisingly, most important methodological advancements in more than half century of molecular modeling are various implementations of these two fundamental principles. In the mainstream classical computational molecular science, tremendous efforts have been invested on two lines of algorithm development. The first is coarse graining, which is to represent multiple basic particles in higher resolution modeling as a single larger and softer particle in lower resolution counterpart, with resulting force fields of partial transferability at the expense of some information loss. The second is enhanced sampling, which realizes "dividing and conquering" and/or "caching" in configurational space with focus either on reaction coordinates and collective variables as in metadynamics and related algorithms, or on the transition matrix and state discretization as in Markov state models. For this line of algorithms, spatial resolution is maintained but results are not transferable. Deep learning has been utilized to realize more efficient and accurate ways of "dividing and conquering" and "caching" along these two lines of algorithmic research. We proposed and demonstrated the local free energy landscape approach, a new framework for classical computational molecular science. This framework is based on a third class of algorithm that facilitates molecular modeling through partially transferable in resolution "caching" of distributions for local clusters of molecular degrees of freedom. Differences, connections and potential interactions among these three algorithmic directions are discussed, with the hope to stimulate development of more elegant, efficient and reliable formulations and algorithms for "dividing and conquering" and "caching" in complex molecular systems.

Structural resolution of switchable states of a de novo peptide assembly

De novo protein design is advancing rapidly. However, most designs are for single states. Here we report a de novo designed peptide that forms multiple α-helical-bundle states that are accessible and interconvertible under the same conditions. Usually in such designs amphipathic α helices associate to form compact structures with consolidated hydrophobic cores. However, recent rational and computational designs have delivered open α-helical barrels with functionalisable cavities. By placing glycine judiciously in the helical interfaces of an α-helical barrel, we obtain both open and compact states in a single protein crystal. Molecular dynamics simulations indicate a free-energy landscape with multiple and interconverting states. Together, these findings suggest a frustrated system in which steric interactions that maintain the open barrel and the hydrophobic effect that drives complete collapse are traded-off. Indeed, addition of a hydrophobic co-solvent that can bind within the barrel affects the switch between the states both in silico and experimentally.

So far most of the de novo designed proteins are for single states only. Here, the authors present the de novo design and crystal structure determination of a coiled-coil peptide that assembles into multiple, distinct conformational states under the same conditions and further characterise its properties with biophysical experiments, NMR and MD simulations.

Calmodulin acts as a state-dependent switch to control a cardiac potassium channel opening

Calmodulin (CaM) and phosphatidylinositol 4,5-bisphosphate (PIP₂) are potent regulators of the voltage-gated potassium channel KCNQ1 (K_V7.1), which conducts the cardiac I _Ks current. Although cryo-electron microscopy structures revealed intricate interactions between the KCNQ1 voltage-sensing domain (VSD), CaM, and PIP₂, the functional consequences of these interactions remain unknown. Here, we show that CaM-VSD interactions act as a state-dependent switch to control KCNQ1 pore opening. Combined electrophysiology and molecular dynamics network analysis suggest that VSD transition into the fully activated state allows PIP₂ to compete with CaM for binding to VSD. This leads to conformational changes that alter VSD-pore coupling to stabilize open states. We identify a motif in the KCNQ1 cytosolic domain, which works downstream of CaM-VSD interactions to facilitate the conformational change. Our findings suggest a gating mechanism that integrates PIP₂ and CaM in KCNQ1 voltage-dependent activation, yielding insights into how KCNQ1 gains the phenotypes critical for its physiological function.

Modeling the Role of a Flexible Loop and Active Site Side Chains in Hydride Transfer Catalyzed by Glycerol-3-phosphate Dehydrogenase

Glycerol-3-phosphate dehydrogenase is a biomedically important enzyme that plays a crucial role in lipid biosynthesis. It is activated by a ligand-gated conformational change that is necessary for the enzyme to reach a catalytically competent conformation capable of efficient transition-state stabilization. While the human form (hlGPDH) has been the subject of extensive structural and biochemical studies, corresponding computational studies to support and extend experimental observations have been lacking. We perform here detailed empirical valence bond and Hamiltonian replica exchange molecular dynamics simulations of wild-type hlGPDH and its variants, as well as providing a crystal structure of the binary hlGPDH·NAD R269A variant where the enzyme is present in the open conformation. We estimated the activation free energies for the hydride transfer reaction in wild-type and substituted hlGPDH and investigated the effect of mutations on catalysis from a detailed structural study. In particular, the K120A and R269A variants increase both the volume and solvent exposure of the active site, with concomitant loss of catalytic activity. In addition, the R269 side chain interacts with both the Q295 side chain on the catalytic loop, and the substrate phosphodianion. Our structural data and simulations illustrate the critical role of this side chain in facilitating the closure of hlGPDH into a catalytically competent conformation, through modulating the flexibility of a key catalytic loop (292-LNGQKL-297). This, in turn, rationalizes a tremendous 41,000 fold decrease experimentally in the turnover number, k_cat, upon truncating this residue, as loop closure is essential for both correct positioning of key catalytic residues in the active site, as well as sequestering the active site from the solvent. Taken together, our data highlight the importance of this ligand-gated conformational change in catalysis, a feature that can be exploited both for protein engineering and for the design of allosteric inhibitors targeting this biomedically important enzyme.